Extensive related works on antenna array diagnosis have been proposed, including compressed sensing based techniques [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]} and deep learning based techniques [8]}.
The main idea of compressed sensing based diagnostic techniques is to use known channel state information (CSI) to generate fault-free reference signal and then subtract it from the received signal.
Therefore, the differential signal contains the information of faulty antenna elements, which can then be estimated from compressed measurements.
The work [1]} is the first to introduce compressed sensing to array diagnosis.
Improvements over [1]} include modifications in sampling methods [2]}, [3]}, [4]} and refinements in sparse recovery algorithms [5]}, [6]}, [7]}.
In particular, the work in [17]} extended antenna-only diagnosis to joint diagnosis of antenna, phase shifter, and RF chain for hybrid beamforming (HBF) systems.
Deep-learning based diagnostic techniques include [8]}, which utilizes a convolutional neural network (CNN) to detect the abnormality in the distribution of received signal and then locate faulty antenna elements.
